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Autonomous Navigation on Icy Moons and Ocean Worlds

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: 80NSSC19C0553
Agency Tracking Number: 193821
Amount: $124,903.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: T4
Solicitation Number: STTR_19_P1
Solicitation Year: 2019
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-08-19
Award End Date (Contract End Date): 2020-09-18
Small Business Information
4591 Hwy 20E, Premier Building, Suite 202J
Niceville, FL 32578-4063
United States
DUNS: 045812577
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Michael Veth
 (937) 529-8384
Business Contact
 Ken Albright
Title: michael_veth
Phone: (228) 813-6127
Research Institution
 Georgia Institute of Technology-Main Campus
225 North Ave
Atlanta, GA 00000-0000
United States

 Federally Funded R&D Center (FFRDC)

Technology currently used for terrestrial navigation is extremely limited in the challenging environments on icy moons and ocean worlds such as Europa. Autonomous platforms used to collect data from beneath these ice and ocean surfaces cannot depend on access to satellite or any other electromagnetic communication. Autonomous systems in these environments are required to perform highly-complex tasks over long mission durationsnbsp;through which communication blackout can be expected, and therefore must have access to precise navigation and localization information. Sensor fusion of standard sensing technology such as accelerometers, gyroscopes, doppler velocity logs, and magnetometers must be augmented with more advanced navigation techniques such as novel acoustic approaches, visual navigation and advanced path planning to meet the requirements of these missions. Here we propose to develop such a navigation and path planning system to advance the autonomous capabilities of a robotic system deployed to remote icy moons and ocean planets using tools commonly used in terrestrial applications such as visual navigation, factor graphs, and deep learning. This technology will maximize the science return for future missions to these moons and planets, as well as advance the state-of-the-art technology available for earth science applications.

* Information listed above is at the time of submission. *

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